Overview

Dataset statistics

Number of variables16
Number of observations840520
Missing cells587717
Missing cells (%)4.4%
Duplicate rows4091
Duplicate rows (%)0.5%
Total size in memory645.8 MiB
Average record size in memory805.7 B

Variable types

Numeric7
Categorical9

Alerts

Dataset has 4091 (0.5%) duplicate rowsDuplicates
Fecha has a high cardinality: 2341 distinct valuesHigh cardinality
Delito has a high cardinality: 349 distinct valuesHigh cardinality
Colonia has a high cardinality: 2009 distinct valuesHigh cardinality
Alcaldia has a high cardinality: 459 distinct valuesHigh cardinality
idCarpeta is highly overall correlated with AñoHigh correlation
Dia is highly overall correlated with MesHigh correlation
Año is highly overall correlated with idCarpetaHigh correlation
Mes is highly overall correlated with DiaHigh correlation
CalidadJuridica is highly overall correlated with Categoria and 1 other fieldsHigh correlation
TipoPersona is highly overall correlated with CalidadJuridicaHigh correlation
Categoria is highly overall correlated with CalidadJuridicaHigh correlation
longitud is highly overall correlated with latitudHigh correlation
latitud is highly overall correlated with longitudHigh correlation
Colonia has 38296 (4.6%) missing valuesMissing
Sexo has 161205 (19.2%) missing valuesMissing
Edad has 302781 (36.0%) missing valuesMissing
longitud has 38086 (4.5%) missing valuesMissing
latitud has 38084 (4.5%) missing valuesMissing
idCarpeta is highly skewed (γ1 = 24.97368042)Skewed
Hora has 11988 (1.4%) zerosZeros

Reproduction

Analysis started2022-11-28 20:03:46.124249
Analysis finished2022-11-28 20:05:32.178642
Duration1 minute and 46.05 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

idCarpeta
Real number (ℝ)

HIGH CORRELATION
SKEWED

Distinct785490
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8770736.1
Minimum8118324
Maximum84881009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 MiB
2022-11-28T14:05:32.337176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8118324
5-th percentile8365696
Q18537366.8
median8767879.5
Q39001345.5
95-th percentile9182364.1
Maximum84881009
Range76762685
Interquartile range (IQR)463978.75

Descriptive statistics

Standard deviation275971.26
Coefficient of variation (CV)0.031465005
Kurtosis6881.4047
Mean8770736.1
Median Absolute Deviation (MAD)232084.5
Skewness24.97368
Sum7.3719791 × 1012
Variance7.6160134 × 1010
MonotonicityNot monotonic
2022-11-28T14:05:32.676708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9125284 26
 
< 0.1%
9057330 22
 
< 0.1%
8718889 19
 
< 0.1%
8929524 17
 
< 0.1%
9124498 16
 
< 0.1%
9075349 15
 
< 0.1%
8406552 15
 
< 0.1%
9059545 14
 
< 0.1%
9119601 13
 
< 0.1%
8808098 13
 
< 0.1%
Other values (785480) 840350
> 99.9%
ValueCountFrequency (%)
8118324 1
< 0.1%
8236085 1
< 0.1%
8312585 1
< 0.1%
8322418 1
< 0.1%
8322419 1
< 0.1%
8322420 1
< 0.1%
8322421 1
< 0.1%
8322422 1
< 0.1%
8322425 1
< 0.1%
8322426 1
< 0.1%
ValueCountFrequency (%)
84881009 1
< 0.1%
9226877 1
< 0.1%
9226876 1
< 0.1%
9226875 1
< 0.1%
9226874 1
< 0.1%
9226873 1
< 0.1%
9226872 1
< 0.1%
9226871 1
< 0.1%
9226870 1
< 0.1%
9226869 1
< 0.1%

Dia
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing343
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6.0594494
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 MiB
2022-11-28T14:05:32.808808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4366242
Coefficient of variation (CV)0.56715123
Kurtosis-1.1797016
Mean6.0594494
Median Absolute Deviation (MAD)3
Skewness0.18681118
Sum5091010
Variance11.810386
MonotonicityNot monotonic
2022-11-28T14:05:32.899376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 87183
10.4%
1 80659
9.6%
2 80104
9.5%
5 79526
9.5%
4 75365
9.0%
6 74401
8.9%
8 62318
7.4%
10 61394
7.3%
9 60117
7.2%
11 60069
7.1%
Other values (2) 119041
14.2%
ValueCountFrequency (%)
1 80659
9.6%
2 80104
9.5%
3 87183
10.4%
4 75365
9.0%
5 79526
9.5%
6 74401
8.9%
7 59938
7.1%
8 62318
7.4%
9 60117
7.2%
10 61394
7.3%
ValueCountFrequency (%)
12 59103
7.0%
11 60069
7.1%
10 61394
7.3%
9 60117
7.2%
8 62318
7.4%
7 59938
7.1%
6 74401
8.9%
5 79526
9.5%
4 75365
9.0%
3 87183
10.4%

Mes
Categorical

Distinct12
Distinct (%)< 0.1%
Missing343
Missing (%)< 0.1%
Memory size57.1 MiB
Marzo
87183 
Enero
80659 
Febrero
80104 
Mayo
79526 
Abril
75365 
Other values (7)
437340 

Length

Max length10
Median length9
Mean length6.2414777
Min length4

Characters and Unicode

Total characters5243946
Distinct characters27
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgosto
2nd rowDiciembre
3rd rowDiciembre
4th rowEnero
5th rowEnero

Common Values

ValueCountFrequency (%)
Marzo 87183
10.4%
Enero 80659
9.6%
Febrero 80104
9.5%
Mayo 79526
9.5%
Abril 75365
9.0%
Junio 74401
8.9%
Agosto 62318
7.4%
Octubre 61394
7.3%
Septiembre 60117
7.2%
Noviembre 60069
7.1%
Other values (2) 119041
14.2%

Length

2022-11-28T14:05:33.004407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
marzo 87183
10.4%
enero 80659
9.6%
febrero 80104
9.5%
mayo 79526
9.5%
abril 75365
9.0%
junio 74401
8.9%
agosto 62318
7.4%
octubre 61394
7.3%
septiembre 60117
7.2%
noviembre 60069
7.1%
Other values (2) 119041
14.2%

Most occurring characters

ValueCountFrequency (%)
e 720956
13.7%
o 646516
12.3%
r 644098
12.3%
i 448096
 
8.5%
b 396152
 
7.6%
u 195733
 
3.7%
t 183829
 
3.5%
m 179289
 
3.4%
M 166709
 
3.2%
a 166709
 
3.2%
Other values (17) 1495859
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4403769
84.0%
Uppercase Letter 840177
 
16.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 720956
16.4%
o 646516
14.7%
r 644098
14.6%
i 448096
10.2%
b 396152
9.0%
u 195733
 
4.4%
t 183829
 
4.2%
m 179289
 
4.1%
a 166709
 
3.8%
n 155060
 
3.5%
Other values (8) 667331
15.2%
Uppercase Letter
ValueCountFrequency (%)
M 166709
19.8%
A 137683
16.4%
J 134339
16.0%
E 80659
9.6%
F 80104
9.5%
O 61394
 
7.3%
S 60117
 
7.2%
N 60069
 
7.1%
D 59103
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5243946
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 720956
13.7%
o 646516
12.3%
r 644098
12.3%
i 448096
 
8.5%
b 396152
 
7.6%
u 195733
 
3.7%
t 183829
 
3.5%
m 179289
 
3.4%
M 166709
 
3.2%
a 166709
 
3.2%
Other values (17) 1495859
28.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5243946
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 720956
13.7%
o 646516
12.3%
r 644098
12.3%
i 448096
 
8.5%
b 396152
 
7.6%
u 195733
 
3.7%
t 183829
 
3.5%
m 179289
 
3.4%
M 166709
 
3.2%
a 166709
 
3.2%
Other values (17) 1495859
28.5%

Año
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing343
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2020.1683
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 MiB
2022-11-28T14:05:33.102054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2019
Q12019
median2020
Q32021
95-th percentile2022
Maximum2022
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1101757
Coefficient of variation (CV)0.00054954615
Kurtosis-0.6204538
Mean2020.1683
Median Absolute Deviation (MAD)1
Skewness-0.0045060485
Sum1.697299 × 109
Variance1.2324901
MonotonicityNot monotonic
2022-11-28T14:05:33.178635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2019 260515
31.0%
2021 233266
27.8%
2020 213883
25.4%
2022 110035
13.1%
2018 17631
 
2.1%
2017 3241
 
0.4%
2016 1606
 
0.2%
(Missing) 343
 
< 0.1%
ValueCountFrequency (%)
2016 1606
 
0.2%
2017 3241
 
0.4%
2018 17631
 
2.1%
2019 260515
31.0%
2020 213883
25.4%
2021 233266
27.8%
2022 110035
13.1%
ValueCountFrequency (%)
2022 110035
13.1%
2021 233266
27.8%
2020 213883
25.4%
2019 260515
31.0%
2018 17631
 
2.1%
2017 3241
 
0.4%
2016 1606
 
0.2%

Fecha
Categorical

Distinct2341
Distinct (%)0.3%
Missing343
Missing (%)< 0.1%
Memory size60.1 MiB
01/01/2021
 
1199
01/01/2020
 
1114
01/04/2019
 
1030
15/03/2019
 
951
15/02/2019
 
948
Other values (2336)
834935 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters8401770
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)< 0.1%

Sample

1st row29/08/2018
2nd row15/12/2018
3rd row22/12/2018
4th row04/01/2019
5th row03/01/2019

Common Values

ValueCountFrequency (%)
01/01/2021 1199
 
0.1%
01/01/2020 1114
 
0.1%
01/04/2019 1030
 
0.1%
15/03/2019 951
 
0.1%
15/02/2019 948
 
0.1%
01/03/2019 941
 
0.1%
01/02/2019 934
 
0.1%
15/05/2019 925
 
0.1%
01/07/2019 921
 
0.1%
01/08/2019 914
 
0.1%
Other values (2331) 830300
98.8%

Length

2022-11-28T14:05:33.277345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01/01/2021 1199
 
0.1%
01/01/2020 1114
 
0.1%
01/04/2019 1030
 
0.1%
15/03/2019 951
 
0.1%
15/02/2019 948
 
0.1%
01/03/2019 941
 
0.1%
01/02/2019 934
 
0.1%
15/05/2019 925
 
0.1%
01/07/2019 921
 
0.1%
01/08/2019 914
 
0.1%
Other values (2331) 830300
98.8%

Most occurring characters

ValueCountFrequency (%)
0 2110064
25.1%
2 1998298
23.8%
/ 1680354
20.0%
1 1219272
14.5%
9 398476
 
4.7%
3 208312
 
2.5%
5 165844
 
2.0%
8 162041
 
1.9%
6 157262
 
1.9%
4 156942
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6721416
80.0%
Other Punctuation 1680354
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2110064
31.4%
2 1998298
29.7%
1 1219272
18.1%
9 398476
 
5.9%
3 208312
 
3.1%
5 165844
 
2.5%
8 162041
 
2.4%
6 157262
 
2.3%
4 156942
 
2.3%
7 144905
 
2.2%
Other Punctuation
ValueCountFrequency (%)
/ 1680354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8401770
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2110064
25.1%
2 1998298
23.8%
/ 1680354
20.0%
1 1219272
14.5%
9 398476
 
4.7%
3 208312
 
2.5%
5 165844
 
2.0%
8 162041
 
1.9%
6 157262
 
1.9%
4 156942
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8401770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2110064
25.1%
2 1998298
23.8%
/ 1680354
20.0%
1 1219272
14.5%
9 398476
 
4.7%
3 208312
 
2.5%
5 165844
 
2.0%
8 162041
 
1.9%
6 157262
 
1.9%
4 156942
 
1.9%

Hora
Real number (ℝ)

Distinct1688
Distinct (%)0.2%
Missing333
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean47470.789
Minimum0
Maximum86340
Zeros11988
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size12.8 MiB
2022-11-28T14:05:33.393113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7200
Q135100
median46800
Q364740
95-th percentile79200
Maximum86340
Range86340
Interquartile range (IQR)29640

Descriptive statistics

Standard deviation21046.011
Coefficient of variation (CV)0.44334656
Kurtosis-0.52250632
Mean47470.789
Median Absolute Deviation (MAD)14400
Skewness-0.28746314
Sum3.9884339 × 1010
Variance4.4293456 × 108
MonotonicityNot monotonic
2022-11-28T14:05:33.511536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43200 72177
 
8.6%
36000 31856
 
3.8%
39600 19059
 
2.3%
54000 18934
 
2.3%
50400 18414
 
2.2%
32400 18406
 
2.2%
57600 17106
 
2.0%
64800 16101
 
1.9%
46800 15839
 
1.9%
61200 15833
 
1.9%
Other values (1678) 596462
71.0%
ValueCountFrequency (%)
0 11988
1.4%
1 7
 
< 0.1%
2 7
 
< 0.1%
3 7
 
< 0.1%
4 6
 
< 0.1%
5 7
 
< 0.1%
6 6
 
< 0.1%
7 6
 
< 0.1%
8 4
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
86340 238
 
< 0.1%
86280 82
 
< 0.1%
86220 48
 
< 0.1%
86160 42
 
< 0.1%
86100 485
0.1%
86040 32
 
< 0.1%
85980 38
 
< 0.1%
85920 40
 
< 0.1%
85860 35
 
< 0.1%
85800 1102
0.1%

Delito
Categorical

Distinct349
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.4 MiB
VIOLENCIA FAMILIAR
114006 
FRAUDE
59069 
AMENAZAS
55926 
ROBO DE OBJETOS
 
41868
ROBO A TRANSEUNTE EN VIA PUBLICA CON VIOLENCIA
 
38160
Other values (344)
531491 

Length

Max length213
Median length91
Mean length30.211439
Min length4

Characters and Unicode

Total characters25393319
Distinct characters47
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowFRAUDE
2nd rowPRODUCCIÓN, IMPRESIÓN, ENAJENACIÓN, DISTRIBUCIÓN, ALTERACIÓN O FALSIFICACIÓN DE TÍTULOS AL PORTADOR, DOCUMENTOS DE CRÉDITO PÚBLICOS O VALES DE CANJE
3rd rowROBO A TRANSEUNTE SALIENDO DEL BANCO CON VIOLENCIA
4th rowROBO DE VEHICULO DE SERVICIO PARTICULAR SIN VIOLENCIA
5th rowROBO DE MOTOCICLETA SIN VIOLENCIA

Common Values

ValueCountFrequency (%)
VIOLENCIA FAMILIAR 114006
 
13.6%
FRAUDE 59069
 
7.0%
AMENAZAS 55926
 
6.7%
ROBO DE OBJETOS 41868
 
5.0%
ROBO A TRANSEUNTE EN VIA PUBLICA CON VIOLENCIA 38160
 
4.5%
ROBO A NEGOCIO SIN VIOLENCIA 29565
 
3.5%
ROBO DE ACCESORIOS DE AUTO 27498
 
3.3%
ROBO DE OBJETOS DEL INTERIOR DE UN VEHICULO 21302
 
2.5%
ROBO A NEGOCIO CON VIOLENCIA 20492
 
2.4%
ROBO DE VEHICULO DE SERVICIO PARTICULAR SIN VIOLENCIA 17196
 
2.0%
Other values (339) 415438
49.4%

Length

2022-11-28T14:05:33.661181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 438736
 
11.2%
violencia 331576
 
8.5%
robo 331502
 
8.5%
a 227978
 
5.8%
con 116526
 
3.0%
familiar 114006
 
2.9%
sin 106548
 
2.7%
en 99102
 
2.5%
por 84513
 
2.2%
negocio 71650
 
1.8%
Other values (481) 1981454
50.8%

Most occurring characters

ValueCountFrequency (%)
3063103
12.1%
O 2784411
11.0%
A 2453243
9.7%
I 2384482
9.4%
E 2283658
 
9.0%
N 1699843
 
6.7%
R 1369313
 
5.4%
C 1358867
 
5.4%
S 1112839
 
4.4%
L 1086493
 
4.3%
Other values (37) 5797067
22.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 22177904
87.3%
Space Separator 3063103
 
12.1%
Other Punctuation 88998
 
0.4%
Open Punctuation 21229
 
0.1%
Close Punctuation 21229
 
0.1%
Decimal Number 11472
 
< 0.1%
Math Symbol 8823
 
< 0.1%
Control 561
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 2784411
12.6%
A 2453243
11.1%
I 2384482
10.8%
E 2283658
10.3%
N 1699843
 
7.7%
R 1369313
 
6.2%
C 1358867
 
6.1%
S 1112839
 
5.0%
L 1086493
 
4.9%
D 1007277
 
4.5%
Other values (21) 4637478
20.9%
Decimal Number
ValueCountFrequency (%)
0 5882
51.3%
9 2941
25.6%
3 1553
 
13.5%
1 952
 
8.3%
8 144
 
1.3%
Other Punctuation
ValueCountFrequency (%)
, 75709
85.1%
/ 12887
 
14.5%
. 402
 
0.5%
Math Symbol
ValueCountFrequency (%)
< 2941
33.3%
+ 2941
33.3%
> 2941
33.3%
Control
ValueCountFrequency (%)
 392
69.9%
 169
30.1%
Space Separator
ValueCountFrequency (%)
3063103
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21229
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21229
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22177904
87.3%
Common 3215415
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 2784411
12.6%
A 2453243
11.1%
I 2384482
10.8%
E 2283658
10.3%
N 1699843
 
7.7%
R 1369313
 
6.2%
C 1358867
 
6.1%
S 1112839
 
5.0%
L 1086493
 
4.9%
D 1007277
 
4.5%
Other values (21) 4637478
20.9%
Common
ValueCountFrequency (%)
3063103
95.3%
, 75709
 
2.4%
( 21229
 
0.7%
) 21229
 
0.7%
/ 12887
 
0.4%
0 5882
 
0.2%
< 2941
 
0.1%
+ 2941
 
0.1%
9 2941
 
0.1%
> 2941
 
0.1%
Other values (6) 3612
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25198588
99.2%
None 194731
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3063103
12.2%
O 2784411
11.0%
A 2453243
9.7%
I 2384482
9.5%
E 2283658
9.1%
N 1699843
 
6.7%
R 1369313
 
5.4%
C 1358867
 
5.4%
S 1112839
 
4.4%
L 1086493
 
4.3%
Other values (28) 5602336
22.2%
None
ValueCountFrequency (%)
Ó 95220
48.9%
Ñ 35884
 
18.4%
Ú 22144
 
11.4%
Á 14780
 
7.6%
Í 11704
 
6.0%
É 10936
 
5.6%
à 3502
 
1.8%
 392
 
0.2%
 169
 
0.1%

CalidadJuridica
Categorical

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size65.3 MiB
VICTIMA Y DENUNCIANTE
542855 
OFENDIDO
132812 
VICTIMA
96709 
LESIONADO
 
24280
AGRAVIADO
 
18673
Other values (2)
 
25190

Length

Max length22
Median length21
Mean length16.455263
Min length7

Characters and Unicode

Total characters13830961
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOFENDIDO
2nd rowVICTIMA Y DENUNCIANTE
3rd rowVICTIMA Y DENUNCIANTE
4th rowVICTIMA Y DENUNCIANTE
5th rowVICTIMA

Common Values

ValueCountFrequency (%)
VICTIMA Y DENUNCIANTE 542855
64.6%
OFENDIDO 132812
 
15.8%
VICTIMA 96709
 
11.5%
LESIONADO 24280
 
2.9%
AGRAVIADO 18673
 
2.2%
CADAVER 16614
 
2.0%
OFENDIDO Y DENUNCIANTE 8576
 
1.0%
(Missing) 1
 
< 0.1%

Length

2022-11-28T14:05:33.789025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-28T14:05:33.926713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
victima 639564
32.9%
y 551431
28.4%
denunciante 551431
28.4%
ofendido 141388
 
7.3%
lesionado 24280
 
1.2%
agraviado 18673
 
1.0%
cadaver 16614
 
0.9%

Most occurring characters

ValueCountFrequency (%)
I 2014900
14.6%
N 1819961
13.2%
A 1304522
9.4%
E 1285144
9.3%
C 1207609
8.7%
T 1190995
8.6%
1102862
8.0%
D 893774
6.5%
V 674851
 
4.9%
M 639564
 
4.6%
Other values (8) 1696779
12.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12728099
92.0%
Space Separator 1102862
 
8.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 2014900
15.8%
N 1819961
14.3%
A 1304522
10.2%
E 1285144
10.1%
C 1207609
9.5%
T 1190995
9.4%
D 893774
7.0%
V 674851
 
5.3%
M 639564
 
5.0%
Y 551431
 
4.3%
Other values (7) 1145348
9.0%
Space Separator
ValueCountFrequency (%)
1102862
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12728099
92.0%
Common 1102862
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 2014900
15.8%
N 1819961
14.3%
A 1304522
10.2%
E 1285144
10.1%
C 1207609
9.5%
T 1190995
9.4%
D 893774
7.0%
V 674851
 
5.3%
M 639564
 
5.0%
Y 551431
 
4.3%
Other values (7) 1145348
9.0%
Common
ValueCountFrequency (%)
1102862
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13830961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 2014900
14.6%
N 1819961
13.2%
A 1304522
9.4%
E 1285144
9.3%
C 1207609
8.7%
T 1190995
8.6%
1102862
8.0%
D 893774
6.5%
V 674851
 
4.9%
M 639564
 
4.6%
Other values (8) 1696779
12.3%

Categoria
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size74.1 MiB
DELITO DE BAJO IMPACTO
683259 
ROBO A TRANSEUNTE EN VÍA PÚBLICA CON Y SIN VIOLENCIA
 
44096
ROBO DE VEHÍCULO CON Y SIN VIOLENCIA
 
34601
ROBO A NEGOCIO CON VIOLENCIA
 
22591
HECHO NO DELICTIVO
 
13723
Other values (15)
 
42250

Length

Max length61
Median length22
Mean length24.929235
Min length9

Characters and Unicode

Total characters20953521
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDELITO DE BAJO IMPACTO
2nd rowDELITO DE BAJO IMPACTO
3rd rowROBO A CUENTAHABIENTE SALIENDO DEL CAJERO CON VIOLENCIA
4th rowROBO DE VEHÍCULO CON Y SIN VIOLENCIA
5th rowROBO DE VEHÍCULO CON Y SIN VIOLENCIA

Common Values

ValueCountFrequency (%)
DELITO DE BAJO IMPACTO 683259
81.3%
ROBO A TRANSEUNTE EN VÍA PÚBLICA CON Y SIN VIOLENCIA 44096
 
5.2%
ROBO DE VEHÍCULO CON Y SIN VIOLENCIA 34601
 
4.1%
ROBO A NEGOCIO CON VIOLENCIA 22591
 
2.7%
HECHO NO DELICTIVO 13723
 
1.6%
ROBO A REPARTIDOR CON Y SIN VIOLENCIA 10191
 
1.2%
VIOLACIÓN 6396
 
0.8%
ROBO A PASAJERO A BORDO DEL METRO CON Y SIN VIOLENCIA 5640
 
0.7%
HOMICIDIO DOLOSO 5388
 
0.6%
LESIONES DOLOSAS POR DISPARO DE ARMA DE FUEGO 4528
 
0.5%
Other values (10) 10107
 
1.2%

Length

2022-11-28T14:05:34.045761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 731566
19.0%
delito 683259
17.8%
bajo 683259
17.8%
impacto 683259
17.8%
con 126896
 
3.3%
robo 126896
 
3.3%
violencia 126896
 
3.3%
a 101467
 
2.6%
sin 99528
 
2.6%
y 99528
 
2.6%
Other values (37) 379862
9.9%

Most occurring characters

ValueCountFrequency (%)
3001896
14.3%
O 2791334
13.3%
I 1875548
9.0%
E 1816477
8.7%
A 1802226
8.6%
T 1493931
 
7.1%
D 1477064
 
7.0%
C 1089945
 
5.2%
L 933904
 
4.5%
B 871731
 
4.2%
Other values (24) 3799465
18.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17942673
85.6%
Space Separator 3001896
 
14.3%
Decimal Number 3837
 
< 0.1%
Math Symbol 3597
 
< 0.1%
Control 1518
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 2791334
15.6%
I 1875548
10.5%
E 1816477
10.1%
A 1802226
10.0%
T 1493931
8.3%
D 1477064
8.2%
C 1089945
 
6.1%
L 933904
 
5.2%
B 871731
 
4.9%
P 757722
 
4.2%
Other values (16) 3032791
16.9%
Decimal Number
ValueCountFrequency (%)
0 2398
62.5%
9 1199
31.2%
3 240
 
6.3%
Math Symbol
ValueCountFrequency (%)
> 1199
33.3%
< 1199
33.3%
+ 1199
33.3%
Space Separator
ValueCountFrequency (%)
3001896
100.0%
Control
ValueCountFrequency (%)
 1518
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17942673
85.6%
Common 3010848
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 2791334
15.6%
I 1875548
10.5%
E 1816477
10.1%
A 1802226
10.0%
T 1493931
8.3%
D 1477064
8.2%
C 1089945
 
6.1%
L 933904
 
5.2%
B 871731
 
4.9%
P 757722
 
4.2%
Other values (16) 3032791
16.9%
Common
ValueCountFrequency (%)
3001896
99.7%
0 2398
 
0.1%
 1518
 
0.1%
> 1199
 
< 0.1%
< 1199
 
< 0.1%
+ 1199
 
< 0.1%
9 1199
 
< 0.1%
3 240
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20817951
99.4%
None 135570
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3001896
14.4%
O 2791334
13.4%
I 1875548
9.0%
E 1816477
8.7%
A 1802226
8.7%
T 1493931
7.2%
D 1477064
7.1%
C 1089945
 
5.2%
L 933904
 
4.5%
B 871731
 
4.2%
Other values (19) 3663895
17.6%
None
ValueCountFrequency (%)
Í 78697
58.0%
Ú 44096
32.5%
Ó 8542
 
6.3%
à 2717
 
2.0%
 1518
 
1.1%

Colonia
Categorical

HIGH CARDINALITY
MISSING

Distinct2009
Distinct (%)0.3%
Missing38296
Missing (%)4.6%
Memory size68.3 MiB
CENTRO
 
23294
DOCTORES
 
14307
DEL VALLE CENTRO
 
10261
ROMA NORTE
 
8594
MORELOS
 
7944
Other values (2004)
737824 

Length

Max length64
Median length48
Mean length15.986997
Min length3

Characters and Unicode

Total characters12825153
Distinct characters75
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)< 0.1%

Sample

1st rowGUADALUPE INN
2nd rowVICTORIA DE LAS DEMOCRACIAS
3rd rowCOPILCO UNIVERSIDAD ISSSTE
4th rowAGRÍCOLA PANTITLAN
5th rowPROGRESISTA

Common Values

ValueCountFrequency (%)
CENTRO 23294
 
2.8%
DOCTORES 14307
 
1.7%
DEL VALLE CENTRO 10261
 
1.2%
ROMA NORTE 8594
 
1.0%
MORELOS 7944
 
0.9%
BUENAVISTA 7468
 
0.9%
NARVARTE 7287
 
0.9%
AGRÍCOLA ORIENTAL 6864
 
0.8%
POLANCO 6011
 
0.7%
JUÁREZ 6003
 
0.7%
Other values (1999) 704191
83.8%
(Missing) 38296
 
4.6%

Length

2022-11-28T14:05:34.183607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 120803
 
6.1%
san 115981
 
5.9%
santa 56115
 
2.8%
centro 42950
 
2.2%
sección 38794
 
2.0%
la 37631
 
1.9%
del 34199
 
1.7%
lomas 24246
 
1.2%
el 23744
 
1.2%
los 20008
 
1.0%
Other values (1482) 1461001
74.0%

Most occurring characters

ValueCountFrequency (%)
A 1751352
13.7%
1173541
 
9.2%
E 1105412
 
8.6%
O 925676
 
7.2%
N 864683
 
6.7%
L 799489
 
6.2%
R 794380
 
6.2%
S 680468
 
5.3%
C 673732
 
5.3%
I 667978
 
5.2%
Other values (65) 3388442
26.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11514381
89.8%
Space Separator 1173541
 
9.2%
Decimal Number 71817
 
0.6%
Lowercase Letter 14136
 
0.1%
Other Punctuation 13784
 
0.1%
Math Symbol 12147
 
0.1%
Dash Punctuation 8211
 
0.1%
Open Punctuation 7365
 
0.1%
Close Punctuation 7155
 
0.1%
Control 2448
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1751352
15.2%
E 1105412
9.6%
O 925676
 
8.0%
N 864683
 
7.5%
L 799489
 
6.9%
R 794380
 
6.9%
S 680468
 
5.9%
C 673732
 
5.9%
I 667978
 
5.8%
T 605965
 
5.3%
Other values (24) 2645246
23.0%
Lowercase Letter
ValueCountFrequency (%)
a 1828
12.9%
o 1704
12.1%
i 1626
11.5%
l 1140
8.1%
e 1128
8.0%
s 988
7.0%
t 944
6.7%
u 878
 
6.2%
n 864
 
6.1%
c 796
 
5.6%
Other values (8) 2240
15.8%
Decimal Number
ValueCountFrequency (%)
2 19200
26.7%
1 15987
22.3%
0 11396
15.9%
3 9023
12.6%
9 5639
 
7.9%
4 2835
 
3.9%
7 2692
 
3.7%
8 2675
 
3.7%
5 1899
 
2.6%
6 471
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 13274
96.3%
/ 269
 
2.0%
? 241
 
1.7%
Math Symbol
ValueCountFrequency (%)
> 4049
33.3%
+ 4049
33.3%
< 4049
33.3%
Control
ValueCountFrequency (%)
 1494
61.0%
 954
39.0%
Space Separator
ValueCountFrequency (%)
1173541
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8211
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7365
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7155
100.0%
Other Symbol
ValueCountFrequency (%)
° 168
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11528517
89.9%
Common 1296636
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1751352
15.2%
E 1105412
9.6%
O 925676
 
8.0%
N 864683
 
7.5%
L 799489
 
6.9%
R 794380
 
6.9%
S 680468
 
5.9%
C 673732
 
5.8%
I 667978
 
5.8%
T 605965
 
5.3%
Other values (42) 2659382
23.1%
Common
ValueCountFrequency (%)
1173541
90.5%
2 19200
 
1.5%
1 15987
 
1.2%
. 13274
 
1.0%
0 11396
 
0.9%
3 9023
 
0.7%
- 8211
 
0.6%
( 7365
 
0.6%
) 7155
 
0.6%
9 5639
 
0.4%
Other values (13) 25845
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12569355
98.0%
None 255798
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1751352
13.9%
1173541
 
9.3%
E 1105412
 
8.8%
O 925676
 
7.4%
N 864683
 
6.9%
L 799489
 
6.4%
R 794380
 
6.3%
S 680468
 
5.4%
C 673732
 
5.4%
I 667978
 
5.3%
Other values (52) 3132644
24.9%
None
ValueCountFrequency (%)
Ó 103456
40.4%
Á 54556
21.3%
É 37166
 
14.5%
Í 35216
 
13.8%
Ñ 9918
 
3.9%
à 6457
 
2.5%
Ú 6203
 
2.4%
 1494
 
0.6%
 954
 
0.4%
Ü 190
 
0.1%
Other values (3) 188
 
0.1%

Alcaldia
Categorical

Distinct459
Distinct (%)0.1%
Missing1445
Missing (%)0.2%
Memory size61.7 MiB
IZTAPALAPA
128413 
CUAUHTEMOC
121721 
GUSTAVO A MADERO
86656 
BENITO JUAREZ
62366 
ALVARO OBREGON
60058 
Other values (454)
379861 

Length

Max length38
Median length35
Mean length11.991984
Min length4

Characters and Unicode

Total characters10062174
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)< 0.1%

Sample

1st rowALVARO OBREGON
2nd rowAZCAPOTZALCO
3rd rowCOYOACAN
4th rowIZTACALCO
5th rowIZTAPALAPA

Common Values

ValueCountFrequency (%)
IZTAPALAPA 128413
15.3%
CUAUHTEMOC 121721
14.5%
GUSTAVO A MADERO 86656
10.3%
BENITO JUAREZ 62366
7.4%
ALVARO OBREGON 60058
7.1%
COYOACAN 55522
6.6%
MIGUEL HIDALGO 52333
 
6.2%
TLALPAN 50498
 
6.0%
VENUSTIANO CARRANZA 48519
 
5.8%
AZCAPOTZALCO 40477
 
4.8%
Other values (449) 132512
15.8%

Length

2022-11-28T14:05:34.327629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
iztapalapa 128413
 
9.8%
cuauhtemoc 121721
 
9.3%
madero 86662
 
6.6%
gustavo 86656
 
6.6%
a 86656
 
6.6%
juarez 63107
 
4.8%
benito 62366
 
4.8%
obregon 60060
 
4.6%
alvaro 60058
 
4.6%
coyoacan 55522
 
4.2%
Other values (532) 498095
38.0%

Most occurring characters

ValueCountFrequency (%)
A 1937987
19.3%
O 971171
 
9.7%
C 671964
 
6.7%
T 628805
 
6.2%
L 606450
 
6.0%
E 564150
 
5.6%
U 536563
 
5.3%
470246
 
4.7%
I 458959
 
4.6%
R 414324
 
4.1%
Other values (22) 2801555
27.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9591901
95.3%
Space Separator 470246
 
4.7%
Lowercase Letter 16
 
< 0.1%
Other Punctuation 7
 
< 0.1%
Dash Punctuation 2
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1937987
20.2%
O 971171
10.1%
C 671964
 
7.0%
T 628805
 
6.6%
L 606450
 
6.3%
E 564150
 
5.9%
U 536563
 
5.6%
I 458959
 
4.8%
R 414324
 
4.3%
N 412192
 
4.3%
Other values (15) 2389336
24.9%
Lowercase Letter
ValueCountFrequency (%)
u 8
50.0%
l 4
25.0%
m 4
25.0%
Space Separator
ValueCountFrequency (%)
470246
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9591917
95.3%
Common 470257
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1937987
20.2%
O 971171
10.1%
C 671964
 
7.0%
T 628805
 
6.6%
L 606450
 
6.3%
E 564150
 
5.9%
U 536563
 
5.6%
I 458959
 
4.8%
R 414324
 
4.3%
N 412192
 
4.3%
Other values (18) 2389352
24.9%
Common
ValueCountFrequency (%)
470246
> 99.9%
. 7
 
< 0.1%
- 2
 
< 0.1%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10062168
> 99.9%
None 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1937987
19.3%
O 971171
 
9.7%
C 671964
 
6.7%
T 628805
 
6.2%
L 606450
 
6.0%
E 564150
 
5.6%
U 536563
 
5.3%
470246
 
4.7%
I 458959
 
4.6%
R 414324
 
4.1%
Other values (21) 2801549
27.8%
None
ValueCountFrequency (%)
Ñ 6
100.0%

Sexo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing161205
Missing (%)19.2%
Memory size53.8 MiB
Masculino
361936 
Femenino
317379 

Length

Max length9
Median length9
Mean length8.5327955
Min length8

Characters and Unicode

Total characters5796456
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMasculino
2nd rowFemenino
3rd rowMasculino
4th rowMasculino
5th rowMasculino

Common Values

ValueCountFrequency (%)
Masculino 361936
43.1%
Femenino 317379
37.8%
(Missing) 161205
19.2%

Length

2022-11-28T14:05:34.435943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-28T14:05:34.530834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
masculino 361936
53.3%
femenino 317379
46.7%

Most occurring characters

ValueCountFrequency (%)
n 996694
17.2%
i 679315
11.7%
o 679315
11.7%
e 634758
11.0%
M 361936
 
6.2%
a 361936
 
6.2%
s 361936
 
6.2%
c 361936
 
6.2%
u 361936
 
6.2%
l 361936
 
6.2%
Other values (2) 634758
11.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5117141
88.3%
Uppercase Letter 679315
 
11.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 996694
19.5%
i 679315
13.3%
o 679315
13.3%
e 634758
12.4%
a 361936
 
7.1%
s 361936
 
7.1%
c 361936
 
7.1%
u 361936
 
7.1%
l 361936
 
7.1%
m 317379
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
M 361936
53.3%
F 317379
46.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 5796456
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 996694
17.2%
i 679315
11.7%
o 679315
11.7%
e 634758
11.0%
M 361936
 
6.2%
a 361936
 
6.2%
s 361936
 
6.2%
c 361936
 
6.2%
u 361936
 
6.2%
l 361936
 
6.2%
Other values (2) 634758
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5796456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 996694
17.2%
i 679315
11.7%
o 679315
11.7%
e 634758
11.0%
M 361936
 
6.2%
a 361936
 
6.2%
s 361936
 
6.2%
c 361936
 
6.2%
u 361936
 
6.2%
l 361936
 
6.2%
Other values (2) 634758
11.0%

Edad
Real number (ℝ)

Distinct112
Distinct (%)< 0.1%
Missing302781
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean38.840404
Minimum0
Maximum369
Zeros1873
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size12.8 MiB
2022-11-28T14:05:34.626295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q127
median37
Q349
95-th percentile68
Maximum369
Range369
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.271485
Coefficient of variation (CV)0.41893193
Kurtosis0.3442314
Mean38.840404
Median Absolute Deviation (MAD)11
Skewness0.38892028
Sum20886000
Variance264.76124
MonotonicityNot monotonic
2022-11-28T14:05:34.740705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 14712
 
1.8%
28 14484
 
1.7%
29 14132
 
1.7%
27 14039
 
1.7%
31 13913
 
1.7%
32 13869
 
1.7%
35 13816
 
1.6%
26 13708
 
1.6%
33 13676
 
1.6%
34 13400
 
1.6%
Other values (102) 397990
47.4%
(Missing) 302781
36.0%
ValueCountFrequency (%)
0 1873
0.2%
1 820
0.1%
2 1036
0.1%
3 1477
0.2%
4 1601
0.2%
5 1604
0.2%
6 1595
0.2%
7 1539
0.2%
8 1582
0.2%
9 1557
0.2%
ValueCountFrequency (%)
369 1
< 0.1%
258 1
< 0.1%
120 1
< 0.1%
114 1
< 0.1%
111 1
< 0.1%
110 1
< 0.1%
107 1
< 0.1%
104 2
< 0.1%
103 2
< 0.1%
102 2
< 0.1%

TipoPersona
Categorical

Distinct2
Distinct (%)< 0.1%
Missing6114
Missing (%)0.7%
Memory size56.6 MiB
FISICA
659630 
MORAL
174776 

Length

Max length6
Median length6
Mean length5.7905384
Min length5

Characters and Unicode

Total characters4831660
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFISICA
2nd rowFISICA
3rd rowFISICA
4th rowFISICA
5th rowFISICA

Common Values

ValueCountFrequency (%)
FISICA 659630
78.5%
MORAL 174776
 
20.8%
(Missing) 6114
 
0.7%

Length

2022-11-28T14:05:34.851105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-28T14:05:34.947013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
fisica 659630
79.1%
moral 174776
 
20.9%

Most occurring characters

ValueCountFrequency (%)
I 1319260
27.3%
A 834406
17.3%
F 659630
13.7%
S 659630
13.7%
C 659630
13.7%
M 174776
 
3.6%
O 174776
 
3.6%
R 174776
 
3.6%
L 174776
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4831660
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 1319260
27.3%
A 834406
17.3%
F 659630
13.7%
S 659630
13.7%
C 659630
13.7%
M 174776
 
3.6%
O 174776
 
3.6%
R 174776
 
3.6%
L 174776
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 4831660
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 1319260
27.3%
A 834406
17.3%
F 659630
13.7%
S 659630
13.7%
C 659630
13.7%
M 174776
 
3.6%
O 174776
 
3.6%
R 174776
 
3.6%
L 174776
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4831660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 1319260
27.3%
A 834406
17.3%
F 659630
13.7%
S 659630
13.7%
C 659630
13.7%
M 174776
 
3.6%
O 174776
 
3.6%
R 174776
 
3.6%
L 174776
 
3.6%

longitud
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct537438
Distinct (%)67.0%
Missing38086
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean-99.136243
Minimum-99.342357
Maximum-98.946858
Zeros0
Zeros (%)0.0%
Negative802434
Negative (%)95.5%
Memory size12.8 MiB
2022-11-28T14:05:35.070144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-99.342357
5-th percentile-99.236638
Q1-99.17634
median-99.140802
Q3-99.096198
95-th percentile-99.024952
Maximum-98.946858
Range0.39549952
Interquartile range (IQR)0.080142204

Descriptive statistics

Standard deviation0.06199455
Coefficient of variation (CV)-0.00062534697
Kurtosis-0.0085473273
Mean-99.136243
Median Absolute Deviation (MAD)0.03992569
Skewness0.092767595
Sum-79550292
Variance0.0038433242
MonotonicityNot monotonic
2022-11-28T14:05:35.190194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99.14957 255
 
< 0.1%
-99.14838 179
 
< 0.1%
-99.1446389 160
 
< 0.1%
-99.0905297 143
 
< 0.1%
-99.14792 140
 
< 0.1%
-99.14717 119
 
< 0.1%
-99.14133 113
 
< 0.1%
-99.15316 109
 
< 0.1%
-99.14544 90
 
< 0.1%
-99.14512 90
 
< 0.1%
Other values (537428) 801036
95.3%
(Missing) 38086
 
4.5%
ValueCountFrequency (%)
-99.34235742 1
< 0.1%
-99.34146881 1
< 0.1%
-99.34145294 1
< 0.1%
-99.34135616 1
< 0.1%
-99.3413415 1
< 0.1%
-99.34125517 1
< 0.1%
-99.34121132 1
< 0.1%
-99.34072 1
< 0.1%
-99.3405354 2
< 0.1%
-99.34024 1
< 0.1%
ValueCountFrequency (%)
-98.94685791 1
 
< 0.1%
-98.94737408 1
 
< 0.1%
-98.94771605 1
 
< 0.1%
-98.94780786 1
 
< 0.1%
-98.94808643 5
< 0.1%
-98.94813 2
 
< 0.1%
-98.94817114 1
 
< 0.1%
-98.94832 1
 
< 0.1%
-98.9483875 1
 
< 0.1%
-98.94854777 1
 
< 0.1%

latitud
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct540191
Distinct (%)67.3%
Missing38084
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean19.385183
Minimum19.126361
Maximum19.583333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 MiB
2022-11-28T14:05:35.349910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19.126361
5-th percentile19.26385
Q119.3352
median19.386776
Q319.438167
95-th percentile19.494466
Maximum19.583333
Range0.45697268
Interquartile range (IQR)0.10296697

Descriptive statistics

Standard deviation0.071926275
Coefficient of variation (CV)0.0037103739
Kurtosis-0.26534464
Mean19.385183
Median Absolute Deviation (MAD)0.051463613
Skewness-0.20552355
Sum15555369
Variance0.0051733891
MonotonicityNot monotonic
2022-11-28T14:05:35.484933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.42121 260
 
< 0.1%
19.41792 168
 
< 0.1%
19.4183695 160
 
< 0.1%
19.42384 152
 
< 0.1%
19.3739788 143
 
< 0.1%
19.43403 123
 
< 0.1%
19.42449 106
 
< 0.1%
19.36174 98
 
< 0.1%
19.44631 95
 
< 0.1%
19.40188 90
 
< 0.1%
Other values (540181) 801041
95.3%
(Missing) 38084
 
4.5%
ValueCountFrequency (%)
19.12636062 1
< 0.1%
19.12664586 1
< 0.1%
19.12703559 2
< 0.1%
19.12723891 1
< 0.1%
19.12733 1
< 0.1%
19.12755982 1
< 0.1%
19.12778569 1
< 0.1%
19.12781795 1
< 0.1%
19.1278348 1
< 0.1%
19.12796004 1
< 0.1%
ValueCountFrequency (%)
19.5833333 1
< 0.1%
19.58232 1
< 0.1%
19.58111 1
< 0.1%
19.5806 1
< 0.1%
19.57955 2
< 0.1%
19.5792784 1
< 0.1%
19.57926981 1
< 0.1%
19.57888 1
< 0.1%
19.57887705 1
< 0.1%
19.57882592 1
< 0.1%

Interactions

2022-11-28T14:05:21.550652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:04.480718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:07.171979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:11.829152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:14.014249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:17.317223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:19.247109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:22.220064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:05.200261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:08.173118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:12.488765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:14.627825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:17.861394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:19.875135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:22.489898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:05.523380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:08.971556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:12.754288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:16.068473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:18.078697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:20.165167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:22.773973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:05.793356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:09.650352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:13.023398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:16.333917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:18.298567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:20.443120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:22.978536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:06.003866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:10.210784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:13.240285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:16.563215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:18.498485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:20.668427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:23.236598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:06.260008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:10.843699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:13.501638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:16.848376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:18.734150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:20.945709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:23.518829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:06.527065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:11.501339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:13.754833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:17.119843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:18.969674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-28T14:05:21.229347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-11-28T14:05:35.601879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-28T14:05:35.736576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-28T14:05:35.844924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-28T14:05:35.951908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-28T14:05:36.069367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-28T14:05:36.431405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-28T14:05:25.380829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-28T14:05:27.058189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-28T14:05:30.829644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idCarpetaDiaMesAñoFechaHoraDelitoCalidadJuridicaCategoriaColoniaAlcaldiaSexoEdadTipoPersonalongitudlatitud
08324429.008Agosto2018.029/08/201843200.0FRAUDEOFENDIDODELITO DE BAJO IMPACTOGUADALUPE INNALVARO OBREGONMasculino62.0FISICA-99.1831419.36125
18324430.012Diciembre2018.015/12/201854000.0PRODUCCIÓN, IMPRESIÓN, ENAJENACIÓN, DISTRIBUCIÓN, ALTERACIÓN O FALSIFICACIÓN DE TÍTULOS AL PORTADOR, DOCUMENTOS DE CRÉDITO PÚBLICOS O VALES DE CANJEVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOVICTORIA DE LAS DEMOCRACIASAZCAPOTZALCOFemenino38.0FISICA-99.1645819.47181
28324431.012Diciembre2018.022/12/201855800.0ROBO A TRANSEUNTE SALIENDO DEL BANCO CON VIOLENCIAVICTIMA Y DENUNCIANTEROBO A CUENTAHABIENTE SALIENDO DEL CAJERO CON VIOLENCIACOPILCO UNIVERSIDAD ISSSTECOYOACANMasculino42.0FISICA-99.1861119.33797
38324435.001Enero2019.004/01/201921600.0ROBO DE VEHICULO DE SERVICIO PARTICULAR SIN VIOLENCIAVICTIMA Y DENUNCIANTEROBO DE VEHÍCULO CON Y SIN VIOLENCIAAGRÍCOLA PANTITLANIZTACALCOMasculino35.0FISICA-99.0598319.40327
48324438.001Enero2019.003/01/201972000.0ROBO DE MOTOCICLETA SIN VIOLENCIAVICTIMAROBO DE VEHÍCULO CON Y SIN VIOLENCIAPROGRESISTAIZTAPALAPAMasculinoNaNFISICA-99.0632419.35480
58324442.010Octubre2018.012/10/201864800.0PRODUCCIÓN, IMPRESIÓN, ENAJENACIÓN, DISTRIBUCIÓN, ALTERACIÓN O FALSIFICACIÓN DE TÍTULOS AL PORTADOR, DOCUMENTOS DE CRÉDITO PÚBLICOS O VALES DE CANJEOFENDIDODELITO DE BAJO IMPACTOPUEBLO DE LOS REYESCOYOACANFemenino42.0FISICA-99.1601619.33537
68324444.001Enero2019.004/01/201930600.0ROBO A TRANSEUNTE DE CELULAR SIN VIOLENCIAVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOTOLTECAALVARO OBREGONFemenino55.0FISICA-99.1947219.39000
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Duplicate rows

Most frequently occurring

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30169057330.007Julio2019.001/07/20190.0FRAUDEOFENDIDODELITO DE BAJO IMPACTONaNCUAUHTEMOCNaNNaNMORALNaNNaN22
13818808098.011Noviembre2020.024/11/202046800.0AMENAZASVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOCENTROCUAUHTEMOCFemeninoNaNFISICA-99.13666519.43587112
17638866253.003Marzo2021.001/03/202154600.0DISCRIMINACIONVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTODOCTORESCUAUHTEMOCFemeninoNaNFISICA-99.15129519.42487312
21308929524.005Mayo2021.016/05/202136000.0AMENAZASVICTIMADELITO DE BAJO IMPACTOEL MIRADOR (NATIVITAS)XOCHIMILCOFemeninoNaNFISICA-99.09698519.23389511
38039187826.005Mayo2022.009/05/202240500.0TORTURAVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTONaNCUAUHTEMOCMasculinoNaNFISICANaNNaN11
39009202775.005Mayo2022.024/05/202249200.0FRAUDEVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTONaNCUAUHTEMOCMasculinoNaNFISICANaNNaN11
24448969356.007Julio2021.009/07/202153400.0MALTRATO ANIMALVICTIMADELITO DE BAJO IMPACTOSAN ANDRÉSAZCAPOTZALCONaNNaNMORAL-99.18302519.49473110
3178644866.002Febrero2020.013/02/202032400.0LA ADMINISTRACION DE JUSTICIAVICTIMADELITO DE BAJO IMPACTOXOCOBENITO JUAREZMasculinoNaNFISICA-99.16304319.3646479
26378997917.007Julio2021.012/07/202132400.0DESPOJOVICTIMADELITO DE BAJO IMPACTONATIVITASXOCHIMILCOMasculinoNaNFISICA-99.01123519.2494729
34229124498.002Febrero2022.016/02/202237260.0EJERCICIO ILEGAL Y ABANDONO DEL SERVICIO PUBLICOVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTONaNCUAUHTEMOCMasculinoNaNFISICANaNNaN9